def test_mae_accessor_outer_array(ds_dask):
ds = ds_dask.load()
b = ds['b']
ds = ds.drop('b')
dim = 'lat'
actual = mae(ds['a'], b, dim)
expected = ds.xs.mae('a', b, dim)
assert_allclose(actual, expected)
def test_mae_r_xr_dask(a_dask, b_dask, dim):
actual = mae(a_dask, b_dask, dim)
dim, axis = _preprocess(dim)
_a_dask = a_dask.values
_b_dask = b_dask.values
axis = tuple(a_dask.dims.index(d) for d in dim)
res = _mae(_a_dask, _b_dask, axis)
expected = actual.copy()
expected.values = res
assert_allclose(actual, expected)
def test_mae_r_xr(a, b, dim):
actual = mae(a, b, dim)
dim, axis = _preprocess(dim)
_a = a.values
_b = b.values
axis = tuple(a.dims.index(d) for d in dim)
res = _mae(_a, _b, axis)
expected = actual.copy()
expected.values = res
assert_allclose(actual, expected)
def test_mae_r_xr(a, b, dim, weight, weights_ones, weights_latitude):
# Generates subsetted weights to pass in as arg to main function and for the numpy testing.
weights_arg, weights_np = adjust_weights(weight, dim, weights_ones,
weights_latitude)
actual = mae(a, b, dim, weights=weights_arg)
assert actual.chunks is None
dim, axis = _preprocess_dims(dim)
_a = a
_b = b
_weights_np = _preprocess_weights(_a, dim, dim, weights_np)
axis = tuple(a.dims.index(d) for d in dim)
res = _mae(_a.values, _b.values, _weights_np.values, axis)
expected = actual.copy()
expected.values = res
assert_allclose(actual, expected)
def test_mae_same_as_sklearn(a, b):
"""Tests that mean absolute error is same as computed from sklearn."""
xs_mae = mae(a, b, 'time')
sklearn_mae = mean_absolute_error(a, b)
assert np.allclose(xs_mae, sklearn_mae)